eCo-FEV is co-funded by the European Commission under the 7th Framework Programme and extends over a period of 33 months, from September 2012 until May 2015. The project involves 13 partners from UK, ITA, DE and FR: Hitachi Europe (Coordinator), BlueThink, CEA, Centro Ricerche FIAT, Conseil Général de l'Isère, EICT, ENERGRID, Facit Research, Institute for Economic Research and Consulting, Politecnico di Torino, RENAULT, TECNOSITAF and TU Berlin. The overall budget is 4.3 million Euro and co-funding from the European Union is about 3 million Euro.

In the beginning of the project, the use cases and requirements were defined and the design of the functional architecture was started. The development and the following integration of the eCo-FEV sub-systems will be done as a preparation for the system validation. The final phase will focus on testing the system and proceed to a demonstration at two test sites in France and Italy.

Project Content

The objectives of the eCo-FEV project are to simplify the usage of the full electrical vehicles and to appease range anxiety related to the full electrical powertrain concept. To achieve these objectives the eCo-FEV project proposes to play the role of facilitator between travellers and all operators participating in planning and realization of trips involving FEVs. The project proposes services for two classes of users: individual travellers and light urban delivery fleet drivers.

The main use cases for both classes of users are “trip planning” and “trip assistance”. For individual travellers, trips can include a supplementary option of multimodality (i.e. a trip mixing public transport and personal car usage). For urban delivery, the trip planning is encompassed by the more general daily planning for the entire urban delivery fleet.

The principal novel characteristic of eCo-FEV is its employment of data-mining (i.e. cloud based “data collect” and “learning machine”) to optimize the trip planning and of what we term “trip monitoring” to reassure the trip realization. This new approach allows roadmap generations based on knowledge coming from historical data (driver and car behaviors, traffic and weather forecasts) and complete information about availability of charging spots. The real time functionality introduced by the “trip monitoring” permanently verifies the trip progress and the accessibility of the charging points. Due to the trip monitoring the driver will always be proposed the best solution to achieve his trip objectives.

eCo-FEV ecosystem

Another novelty concerns the eCo-FEV business model in which we separate the role of so called "identity provider" from that called "service provider". The "identity providers" are in charge of user subscriptions and "service providers" deliver the services (e.g. charging, parking) to the users recognized by eCo-FEV "identity providers". Some operators can play both roles. Thanks to this approach we hope to limit the number of subscriptions the user has to accomplish, and to simplify the payment processes when the user is not in the vicinity of his home.

Business model flexibility

The proposed novel functionalities rely on technological novelties introduced by the eCo-FEV project. These novelties address the FEV charging technology and the ICT solutions. In the domain of charging technology the eCo-FEV project builds a field test for charge while driving. In the ICT domain, the project introduces "OpenID" (c.f. http://openid.net/specs/openid-authentication-2_0.html) necessary for the identity provider" and "service provider" paradigm deployment, "Mobile IP" (c.f. http://tools.ietf.org/html/rfc2002) ensuring the car addressability in spite of telecommunication access changes and "M2M CoAP" (c.f. http://tools.ietf.org/wg/core/draft-ietf-core-groupcomm/) optimized protocol for data collect functionalities.

eCo-FEV technology

Evaluation methodology

Along with technological development, the project team established a comprehensive evaluation methodology. Various indicators concerning the components, the system, including its operation were defined and their application for the test and overall performance analysis was documented. Among the indicators there are those describing the service quality perceived by users (e.g. demand satisfaction, charging and parking spot queuing, routing performance, rerouting frequency) and those describing the system overall performances (e.g. efficiency of the entire energy chain, charging station occupancy level, system usage statistics).